LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
SP '10 Proceedings of the 2010 IEEE Symposium on Security and Privacy
Energy theft in the advanced metering infrastructure
CRITIS'09 Proceedings of the 4th international conference on Critical information infrastructures security
Multi-vendor penetration testing in the advanced metering infrastructure
Proceedings of the 26th Annual Computer Security Applications Conference
Security Seals on Voting Machines: A Case Study
ACM Transactions on Information and System Security (TISSEC)
Heat pump detection from coarse grained smart meter data with positive and unlabeled learning
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Configuration-based IDS for advanced metering infrastructure
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
Customer-centric energy usage data management and sharing in smart grid systems
Proceedings of the first ACM workshop on Smart energy grid security
Analysis of the impact of data granularity on privacy for the smart grid
Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society
| Hi-index | 0.00 |
Electricity theft is estimated to cost billions of dollars per year in many countries. To reduce electricity theft, electric utilities are leveraging data collected by the new Advanced Metering Infrastructure (AMI) and using data analytics to identify abnormal consumption trends and possible fraud. In this paper, we propose the first threat model for the use of data analytics in detecting electricity theft, and a new metric that leverages this threat model in order to evaluate and compare anomaly detectors. We use real data from an AMI system to validate our approach.